基于多源遥感与机器学习的果树树种识别

doi:10.14088/j.cnki.issn0439-8114.2026.09.029

摘要/Abstract

摘要： 针对复杂果园环境下果树树种遥感识别中存在的物候差异明显、特征冗余以及识别精度不足等问题,以新疆维吾尔自治区阿克苏地区典型果园为研究对象,提出一种融合物候信息与多源遥感数据的果树树种识别方法。利用Sentinel-2光学遥感数据、Sentinel-1雷达数据及地形因子构建多源遥感特征体系,通过物候窗口优化选择具有较高类别可分性的遥感时相,并采用Boruta算法对多源特征进行筛选,以降低特征冗余。在此基础上,引入基于TPE（Tree-structured Parzen Estimator）的贝叶斯优化方法对LightGBM模型的关键超参数进行优化,并结合Stacking集成学习策略构建果树树种分类模型,同时利用SHAP（SHapley Additive exPlanations）方法分析不同遥感特征对分类结果的贡献。该方法在测试集上总体精度达到90.62%,Kappa系数为0.889 3,分类精度高于单一模型方法。多源遥感信息与机器学习方法的结合能够提高果树树种识别精度,可为区域果园资源调查与农业管理提供方法参考。

关键词: 多源遥感, 果树树种识别, 物候信息, 特征选择, 机器学习

Abstract: To address the challenges of complex phenological variations, feature redundancy, and insufficient recognition accuracy in remote sensing identification of fruit tree species under complex orchard environments, a typical orchard area in Aksu, Xinjiang, was selected as the study area, and a fruit tree species identification method integrating phenological information and multi-source remote sensing data was proposed. Multi-source remote sensing features were constructed using Sentinel-2 optical remote sensing data, Sentinel-1 radar data, and terrain factors. Phenological window optimization was used to select remote sensing phases with higher class separability, and the Boruta algorithm was adopted to screen multi-source features and reduce feature redundancy. On this basis, a Bayesian optimization method based on TPE (Tree-structured Parzen Estimator) was introduced to optimize the key hyperparameters of the LightGBM model, and was combined with a Stacking ensemble learning strategy to construct the fruit tree species classification model. Meanwhile, SHAP (SHapley Additive exPlanations) was used to analyze the contribution of different remote sensing features to the classification results. The proposed method achieved an overall accuracy of 90.62% and a Kappa coefficient of 0.889 3 on the test set, and its classification accuracy was higher than that of single-model methods. The combination of multi-source remote sensing information and machine learning methods improved the recognition accuracy of fruit tree species and could provide a methodological reference for regional orchard resource surveys and agricultural management.

Key words: multi-source remote sensing, fruit tree species identification, phenological information, feature selection, machine learning

中图分类号:

S126

王茜, 蒲智, 罗磊, 伊雷霆. 基于多源遥感与机器学习的果树树种识别[J]. 湖北农业科学, 2026, 65(9): 176-185.

WANG Qian, PU Zhi, LUO Lei, YI Lei-ting. Fruit tree species identification based on multi-source remote sensing and machine learning[J]. HUBEI AGRICULTURAL SCIENCES, 2026, 65(9): 176-185.

参考文献

[1] IMMITZER M, VUOLO F, ATZBERGER C.First experience with sentinel-2 data for crop and tree species classifications in Central Europe[J].Remote sensing, 2016, 8(3):166.
[2] FASSNACHT F E, LATIFI H, STEREŃCZAK K, et al. Review of studies on tree species classification from remotely sensed data[J].Remote sensing of environment, 2016, 186: 64-87.
[3] NAGENDRA H.Using remote sensing to assess biodiversity[J].International journal of remote sensing, 2001, 22(12):2377-2400.
[4] BELGIU M, DRǍGUŢ L. Random forest in remote sensing: A review of applications and future directions[J].ISPRS journal of photogrammetry and remote sensing, 2016, 114: 24-31.
[5] MAXWELL A E, WARNER T A, FANG F.Implementation of machine-learning classification in remote sensing: An applied review[J].International journal of remote sensing, 2018,39(9):2784-2817.
[6] BERGEN K M, GOETZ S J, DUBAYAH R O, et al. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions[J].Journal of geophysical research: Biogeosciences, 2009, 114(G2):G00E6.
[7] XUE J, SU B.Significant remote sensing vegetation indices: a review of developments and applications[J].Journal of sensors, 2017, 2017(1):1353691.
[8] USTIN S L, GITELSON A A, JACQUEMOUD S, et al.Retrieval of foliar information about plant pigment systems from high resolution spectroscopy[J].Remote sensing of environment, 2009, 113: S67-S77.
[9] PISHAROTY P R.Introduction to remote sensing[J].Proceedings of the Indian Academy of Sciences Section C: Engineering Sciences, 1983, 6(2):97-107.
[10] MATSUSHITA B, YANG W, CHEN J, et al.Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest[J].Sensors, 2007, 7(11):2636-2651.
[11] ABUBAKAR M A, CHANZY A, FLAMAIN F, et al.Delineation of orchard, vineyard, and olive trees based on phenology metrics derived from time series of sentinel-2[J].Remote sensing, 2023, 15(9):2420.
[12] MA L, LIU Y, ZHANG X L, et al.Deep learning in remote sensing applications: A meta-analysis and review[J].ISPRS journal of photogrammetry and remote sensing, 2019, 152: 166-177.
[13] ZHU X X, TUIA D, MOU L C, et al.Deep learning in remote sensing: A comprehensive review and list of resources[J].IEEE geoscience and remote sensing magazine, 2017, 5(4):8-36.
[14] LI W J, FU H H, YU L, et al.Deep learning based oil palm tree detection and counting for high-resolution remote sensing images[J].Remote sensing, 2017, 9(1):22.
[15] PAL M.Random forest classifier for remote sensing classification[J].International journal of remote sensing, 2005, 26(1):217-222.
[16] MOUNTRAKIS G, IM J, OGOLE C.Support vector machines in remote sensing: A review[J].ISPRS journal of photogrammetry and remote sensing, 2011, 66(3):247-259.
[17] CHEN T Q, GUESTRIN C.XGBoost: A scalable tree boosting system[A].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].San Francisco California USA:ACM, 2016.785-794.
[18] KE G L, MENG Q, FINLEY T, et al.LightGBM: A highly efficient gradient boosting decision tree[A].Advances in Neural Information Processing Systems 30[C].Long Beach, USA: Curran Associates, Inc., 2017. 3146-3154.
[19] CHANDRASHEKAR G, SAHIN F.A survey on feature selection methods[J].Computers & electrical engineering, 2014, 40(1):16-28.
[20] BERGSTRA J S, BARDENET R, BENGIO Y, et al.Algorithms for hyper-parameter optimization[A].Advances in Neural Information Processing Systems 24[C].Red Hook, NY: Curran Associates, Inc., 2012.2546-2554.
[21] MOLNAR C.Interpretable machine learning[M].Morrisville: Lulu.com, 2020.
[22] LUNDBERG SM, LEE SI.A unified approach to interpreting model predictions[A].Advances in Neural Information Processing Systems 30[C].Long Beach: Curran Associates Inc., 2017.4768-4777.
[23] RUDIN C.Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J].Nature machine intelligence, 2019, 1(5):206-215.
[24] ZHONG L H, HU L N, ZHOU H.Deep learning based multi-temporal crop classification[J].Remote sensing of environment, 2019, 221: 430-443.
[25] YOU N, DONG J, HUANG J, et al.The 10-m crop type maps in Northeast China during 2017-2019[J].Scientific data,2021,8(1):41.
[26] ASHOURLOO D, NEMATOLLAHI H, HUETE A, et al.A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images[J].Remote sensing of environment, 2022, 280: 113206.
[27] VELOSO A, MERMOZ S, BOUVET A, et al.Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications[J].Remote sensing of environment, 2017, 199: 415-426.
[28] CLAVERIE M, JU J C, MASEK J G, et al.The Harmonized Landsat and Sentinel-2 surface reflectance data set[J].Remote sensing of environment, 2018, 219: 145-161.
[29] KURSA M B, RUDNICKI W R.Feature selection with the Boruta package[J].Journal of statistical software, 2010, 36(11):1-13.
[30] WATANABE S. Tree-structured Parzen estimator: understanding its algorithm components and their roles for better empirical performance[EB/OL].arXiv:2304.11127, 2023.
[31] OZAKI Y, TANIGAKI Y, WATANABE S, et al.Multiobjective tree-structured parzen estimator[J].Journal of artificial intelligence research, 2022, 73: 1209-1250.
[32] GEBREYESUS Y, DALTON D, NIXON S, et al.Machine learning for data center optimizations: Feature selection using shapley additive exPlanation (SHAP)[J].Future internet, 2023, 15(3):88.
[33] MANGALATHU S, HWANG S H, JEON J S.Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach[J].Engineering structures, 2020, 219: 110927.
[34] KURSA M B, JANKOWSKI A,RUDNICKI W R.Boruta-a system for feature selection[J].Fundamenta informaticae,2010,101(4):271-285.
[35] JASPER S, HUGO L, RYAN P.Practical Bayesian optimization of machine learning algorithms[A].Proceedings of the 25th international conference on neural information processing systems[C].Curran Associates Inc, 2012(2):2951.
[36] FALKNER S,KLEIN A,HUTTER F.BOHB: Robust and efficient hyperparameter optimization at scale[A].Proceedings of the 35th international conference on machine learning(ICML 2018)[C].Stockholm, Sweden: Proceedings of Machine Learning Research,2018.1437-1446.
[37] BREIMAN L.Stacked regressions[J].Machine learning, 1996, 24(1):49-64.
[38] DOROGUSH A V, ERSHOV V, GULIN A.CatBoost: gradient boosting with categorical features support[J].2018.DOI:10.48550/arXiv:1810.11363, 2018.